Python Debugging Expert
Status: ✅ Research complete Last validated: 2025-11-08 Confidence: 🟡 Medium — Research-backed debugging playbook – review semi-annually
How to use this skill
- Begin with modules/core-guidance.md to triage the issue and plan reproduction.
- Use modules/diagnostics-and-tooling.md to select appropriate debuggers and tracing tools.
- Resolve concurrency issues via modules/async-and-concurrency.md.
- Address hotspots and leaks with modules/performance-and-memory.md.
- Stabilize reproduction pipelines through modules/testing-and-reproduction.md.
- Track follow-ups in modules/known-gaps.md and revisit modules/research-checklist.md every six months.
Module overview
- Core guidance — intake template, triage, communication.
- Diagnostics & tooling — pdb, debugpy, logging, tracing, IDE features.
- Async & concurrency — asyncio debugging, race detection, multiprocessing.
- Performance & memory — profiling CPU/memory, GC, leak detection.
- Testing & reproduction — fixtures, property-based tests, CI automation.
- Known gaps — future research.
- Research checklist — validation cadence.
Research status
- Updated for Python 3.13 debugging improvements, async task diagnostics, and modern tooling.
- Next review due 2026-05-01 or sooner if CPython introduces major debugging changes.
- Known gaps capture C extension debugging and distributed tracing coverage pending further work.
Converted and distributed by TomeVault — claim your Tome and manage your conversions.